Planning Representation

Planning representation research focuses on developing efficient and robust methods for encoding and manipulating plans, particularly in complex or partially observable environments. Current efforts concentrate on integrating large language models with symbolic planners, leveraging multimodal data (text and images), and developing more expressive planning formalisms to handle uncertainty and real-world constraints. These advancements are crucial for improving the capabilities of autonomous systems, particularly in robotics and AI, by enabling more flexible and adaptable planning in dynamic and open-world scenarios.